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<FONT color="green">001</FONT>    /*<a name="line.1"></a>
<FONT color="green">002</FONT>     * Licensed to the Apache Software Foundation (ASF) under one or more<a name="line.2"></a>
<FONT color="green">003</FONT>     * contributor license agreements.  See the NOTICE file distributed with<a name="line.3"></a>
<FONT color="green">004</FONT>     * this work for additional information regarding copyright ownership.<a name="line.4"></a>
<FONT color="green">005</FONT>     * The ASF licenses this file to You under the Apache License, Version 2.0<a name="line.5"></a>
<FONT color="green">006</FONT>     * (the "License"); you may not use this file except in compliance with<a name="line.6"></a>
<FONT color="green">007</FONT>     * the License.  You may obtain a copy of the License at<a name="line.7"></a>
<FONT color="green">008</FONT>     *<a name="line.8"></a>
<FONT color="green">009</FONT>     *      http://www.apache.org/licenses/LICENSE-2.0<a name="line.9"></a>
<FONT color="green">010</FONT>     *<a name="line.10"></a>
<FONT color="green">011</FONT>     * Unless required by applicable law or agreed to in writing, software<a name="line.11"></a>
<FONT color="green">012</FONT>     * distributed under the License is distributed on an "AS IS" BASIS,<a name="line.12"></a>
<FONT color="green">013</FONT>     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.<a name="line.13"></a>
<FONT color="green">014</FONT>     * See the License for the specific language governing permissions and<a name="line.14"></a>
<FONT color="green">015</FONT>     * limitations under the License.<a name="line.15"></a>
<FONT color="green">016</FONT>     */<a name="line.16"></a>
<FONT color="green">017</FONT>    <a name="line.17"></a>
<FONT color="green">018</FONT>    package org.apache.commons.math.stat.clustering;<a name="line.18"></a>
<FONT color="green">019</FONT>    <a name="line.19"></a>
<FONT color="green">020</FONT>    import java.util.ArrayList;<a name="line.20"></a>
<FONT color="green">021</FONT>    import java.util.Collection;<a name="line.21"></a>
<FONT color="green">022</FONT>    import java.util.List;<a name="line.22"></a>
<FONT color="green">023</FONT>    import java.util.Random;<a name="line.23"></a>
<FONT color="green">024</FONT>    <a name="line.24"></a>
<FONT color="green">025</FONT>    /**<a name="line.25"></a>
<FONT color="green">026</FONT>     * Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.<a name="line.26"></a>
<FONT color="green">027</FONT>     * @param &lt;T&gt; type of the points to cluster<a name="line.27"></a>
<FONT color="green">028</FONT>     * @see &lt;a href="http://en.wikipedia.org/wiki/K-means%2B%2B"&gt;K-means++ (wikipedia)&lt;/a&gt;<a name="line.28"></a>
<FONT color="green">029</FONT>     * @version $Revision: 771076 $ $Date: 2009-05-03 12:28:48 -0400 (Sun, 03 May 2009) $<a name="line.29"></a>
<FONT color="green">030</FONT>     * @since 2.0<a name="line.30"></a>
<FONT color="green">031</FONT>     */<a name="line.31"></a>
<FONT color="green">032</FONT>    public class KMeansPlusPlusClusterer&lt;T extends Clusterable&lt;T&gt;&gt; {<a name="line.32"></a>
<FONT color="green">033</FONT>    <a name="line.33"></a>
<FONT color="green">034</FONT>        /** Random generator for choosing initial centers. */<a name="line.34"></a>
<FONT color="green">035</FONT>        private final Random random;<a name="line.35"></a>
<FONT color="green">036</FONT>    <a name="line.36"></a>
<FONT color="green">037</FONT>        /** Build a clusterer.<a name="line.37"></a>
<FONT color="green">038</FONT>         * @param random random generator to use for choosing initial centers<a name="line.38"></a>
<FONT color="green">039</FONT>         */<a name="line.39"></a>
<FONT color="green">040</FONT>        public KMeansPlusPlusClusterer(final Random random) {<a name="line.40"></a>
<FONT color="green">041</FONT>            this.random = random;<a name="line.41"></a>
<FONT color="green">042</FONT>        }<a name="line.42"></a>
<FONT color="green">043</FONT>    <a name="line.43"></a>
<FONT color="green">044</FONT>        /**<a name="line.44"></a>
<FONT color="green">045</FONT>         * Runs the K-means++ clustering algorithm.<a name="line.45"></a>
<FONT color="green">046</FONT>         * <a name="line.46"></a>
<FONT color="green">047</FONT>         * @param points the points to cluster<a name="line.47"></a>
<FONT color="green">048</FONT>         * @param k the number of clusters to split the data into<a name="line.48"></a>
<FONT color="green">049</FONT>         * @param maxIterations the maximum number of iterations to run the algorithm<a name="line.49"></a>
<FONT color="green">050</FONT>         *     for.  If negative, no maximum will be used<a name="line.50"></a>
<FONT color="green">051</FONT>         * @return a list of clusters containing the points<a name="line.51"></a>
<FONT color="green">052</FONT>         */<a name="line.52"></a>
<FONT color="green">053</FONT>        public List&lt;Cluster&lt;T&gt;&gt; cluster(final Collection&lt;T&gt; points,<a name="line.53"></a>
<FONT color="green">054</FONT>                                        final int k, final int maxIterations) {<a name="line.54"></a>
<FONT color="green">055</FONT>            // create the initial clusters<a name="line.55"></a>
<FONT color="green">056</FONT>            List&lt;Cluster&lt;T&gt;&gt; clusters = chooseInitialCenters(points, k, random);<a name="line.56"></a>
<FONT color="green">057</FONT>            assignPointsToClusters(clusters, points);<a name="line.57"></a>
<FONT color="green">058</FONT>    <a name="line.58"></a>
<FONT color="green">059</FONT>            // iterate through updating the centers until we're done<a name="line.59"></a>
<FONT color="green">060</FONT>            final int max = (maxIterations &lt; 0) ? Integer.MAX_VALUE : maxIterations; <a name="line.60"></a>
<FONT color="green">061</FONT>            for (int count = 0; count &lt; max; count++) {<a name="line.61"></a>
<FONT color="green">062</FONT>                boolean clusteringChanged = false;<a name="line.62"></a>
<FONT color="green">063</FONT>                List&lt;Cluster&lt;T&gt;&gt; newClusters = new ArrayList&lt;Cluster&lt;T&gt;&gt;();<a name="line.63"></a>
<FONT color="green">064</FONT>                for (final Cluster&lt;T&gt; cluster : clusters) {<a name="line.64"></a>
<FONT color="green">065</FONT>                    final T newCenter = cluster.getCenter().centroidOf(cluster.getPoints());<a name="line.65"></a>
<FONT color="green">066</FONT>                    if (!newCenter.equals(cluster.getCenter())) {<a name="line.66"></a>
<FONT color="green">067</FONT>                        clusteringChanged = true;<a name="line.67"></a>
<FONT color="green">068</FONT>                    }<a name="line.68"></a>
<FONT color="green">069</FONT>                    newClusters.add(new Cluster&lt;T&gt;(newCenter));<a name="line.69"></a>
<FONT color="green">070</FONT>                }<a name="line.70"></a>
<FONT color="green">071</FONT>                if (!clusteringChanged) {<a name="line.71"></a>
<FONT color="green">072</FONT>                    return clusters;<a name="line.72"></a>
<FONT color="green">073</FONT>                }<a name="line.73"></a>
<FONT color="green">074</FONT>                assignPointsToClusters(newClusters, points);<a name="line.74"></a>
<FONT color="green">075</FONT>                clusters = newClusters;<a name="line.75"></a>
<FONT color="green">076</FONT>            }<a name="line.76"></a>
<FONT color="green">077</FONT>            return clusters;<a name="line.77"></a>
<FONT color="green">078</FONT>        }<a name="line.78"></a>
<FONT color="green">079</FONT>    <a name="line.79"></a>
<FONT color="green">080</FONT>        /**<a name="line.80"></a>
<FONT color="green">081</FONT>         * Adds the given points to the closest {@link Cluster}.<a name="line.81"></a>
<FONT color="green">082</FONT>         * <a name="line.82"></a>
<FONT color="green">083</FONT>         * @param &lt;T&gt; type of the points to cluster<a name="line.83"></a>
<FONT color="green">084</FONT>         * @param clusters the {@link Cluster}s to add the points to<a name="line.84"></a>
<FONT color="green">085</FONT>         * @param points the points to add to the given {@link Cluster}s<a name="line.85"></a>
<FONT color="green">086</FONT>         */<a name="line.86"></a>
<FONT color="green">087</FONT>        private static &lt;T extends Clusterable&lt;T&gt;&gt; void<a name="line.87"></a>
<FONT color="green">088</FONT>            assignPointsToClusters(final Collection&lt;Cluster&lt;T&gt;&gt; clusters, final Collection&lt;T&gt; points) {<a name="line.88"></a>
<FONT color="green">089</FONT>            for (final T p : points) {<a name="line.89"></a>
<FONT color="green">090</FONT>                Cluster&lt;T&gt; cluster = getNearestCluster(clusters, p);<a name="line.90"></a>
<FONT color="green">091</FONT>                cluster.addPoint(p);<a name="line.91"></a>
<FONT color="green">092</FONT>            }<a name="line.92"></a>
<FONT color="green">093</FONT>        }<a name="line.93"></a>
<FONT color="green">094</FONT>    <a name="line.94"></a>
<FONT color="green">095</FONT>        /**<a name="line.95"></a>
<FONT color="green">096</FONT>         * Use K-means++ to choose the initial centers.<a name="line.96"></a>
<FONT color="green">097</FONT>         * <a name="line.97"></a>
<FONT color="green">098</FONT>         * @param &lt;T&gt; type of the points to cluster<a name="line.98"></a>
<FONT color="green">099</FONT>         * @param points the points to choose the initial centers from<a name="line.99"></a>
<FONT color="green">100</FONT>         * @param k the number of centers to choose<a name="line.100"></a>
<FONT color="green">101</FONT>         * @param random random generator to use<a name="line.101"></a>
<FONT color="green">102</FONT>         * @return the initial centers<a name="line.102"></a>
<FONT color="green">103</FONT>         */<a name="line.103"></a>
<FONT color="green">104</FONT>        private static &lt;T extends Clusterable&lt;T&gt;&gt; List&lt;Cluster&lt;T&gt;&gt;<a name="line.104"></a>
<FONT color="green">105</FONT>            chooseInitialCenters(final Collection&lt;T&gt; points, final int k, final Random random) {<a name="line.105"></a>
<FONT color="green">106</FONT>    <a name="line.106"></a>
<FONT color="green">107</FONT>            final List&lt;T&gt; pointSet = new ArrayList&lt;T&gt;(points);<a name="line.107"></a>
<FONT color="green">108</FONT>            final List&lt;Cluster&lt;T&gt;&gt; resultSet = new ArrayList&lt;Cluster&lt;T&gt;&gt;();<a name="line.108"></a>
<FONT color="green">109</FONT>    <a name="line.109"></a>
<FONT color="green">110</FONT>            // Choose one center uniformly at random from among the data points.<a name="line.110"></a>
<FONT color="green">111</FONT>            final T firstPoint = pointSet.remove(random.nextInt(pointSet.size()));<a name="line.111"></a>
<FONT color="green">112</FONT>            resultSet.add(new Cluster&lt;T&gt;(firstPoint));<a name="line.112"></a>
<FONT color="green">113</FONT>    <a name="line.113"></a>
<FONT color="green">114</FONT>            final double[] dx2 = new double[pointSet.size()];<a name="line.114"></a>
<FONT color="green">115</FONT>            while (resultSet.size() &lt; k) {<a name="line.115"></a>
<FONT color="green">116</FONT>                // For each data point x, compute D(x), the distance between x and <a name="line.116"></a>
<FONT color="green">117</FONT>                // the nearest center that has already been chosen.<a name="line.117"></a>
<FONT color="green">118</FONT>                int sum = 0;<a name="line.118"></a>
<FONT color="green">119</FONT>                for (int i = 0; i &lt; pointSet.size(); i++) {<a name="line.119"></a>
<FONT color="green">120</FONT>                    final T p = pointSet.get(i);<a name="line.120"></a>
<FONT color="green">121</FONT>                    final Cluster&lt;T&gt; nearest = getNearestCluster(resultSet, p);<a name="line.121"></a>
<FONT color="green">122</FONT>                    final double d = p.distanceFrom(nearest.getCenter());<a name="line.122"></a>
<FONT color="green">123</FONT>                    sum += d * d;<a name="line.123"></a>
<FONT color="green">124</FONT>                    dx2[i] = sum;<a name="line.124"></a>
<FONT color="green">125</FONT>                }<a name="line.125"></a>
<FONT color="green">126</FONT>    <a name="line.126"></a>
<FONT color="green">127</FONT>                // Add one new data point as a center. Each point x is chosen with<a name="line.127"></a>
<FONT color="green">128</FONT>                // probability proportional to D(x)2<a name="line.128"></a>
<FONT color="green">129</FONT>                final double r = random.nextDouble() * sum;<a name="line.129"></a>
<FONT color="green">130</FONT>                for (int i = 0 ; i &lt; dx2.length; i++) {<a name="line.130"></a>
<FONT color="green">131</FONT>                    if (dx2[i] &gt;= r) {<a name="line.131"></a>
<FONT color="green">132</FONT>                        final T p = pointSet.remove(i);<a name="line.132"></a>
<FONT color="green">133</FONT>                        resultSet.add(new Cluster&lt;T&gt;(p));<a name="line.133"></a>
<FONT color="green">134</FONT>                        break;<a name="line.134"></a>
<FONT color="green">135</FONT>                    }<a name="line.135"></a>
<FONT color="green">136</FONT>                }<a name="line.136"></a>
<FONT color="green">137</FONT>            }<a name="line.137"></a>
<FONT color="green">138</FONT>    <a name="line.138"></a>
<FONT color="green">139</FONT>            return resultSet;<a name="line.139"></a>
<FONT color="green">140</FONT>    <a name="line.140"></a>
<FONT color="green">141</FONT>        }<a name="line.141"></a>
<FONT color="green">142</FONT>    <a name="line.142"></a>
<FONT color="green">143</FONT>        /**<a name="line.143"></a>
<FONT color="green">144</FONT>         * Returns the nearest {@link Cluster} to the given point<a name="line.144"></a>
<FONT color="green">145</FONT>         * <a name="line.145"></a>
<FONT color="green">146</FONT>         * @param &lt;T&gt; type of the points to cluster<a name="line.146"></a>
<FONT color="green">147</FONT>         * @param clusters the {@link Cluster}s to search<a name="line.147"></a>
<FONT color="green">148</FONT>         * @param point the point to find the nearest {@link Cluster} for<a name="line.148"></a>
<FONT color="green">149</FONT>         * @return the nearest {@link Cluster} to the given point<a name="line.149"></a>
<FONT color="green">150</FONT>         */<a name="line.150"></a>
<FONT color="green">151</FONT>        private static &lt;T extends Clusterable&lt;T&gt;&gt; Cluster&lt;T&gt;<a name="line.151"></a>
<FONT color="green">152</FONT>            getNearestCluster(final Collection&lt;Cluster&lt;T&gt;&gt; clusters, final T point) {<a name="line.152"></a>
<FONT color="green">153</FONT>            double minDistance = Double.MAX_VALUE;<a name="line.153"></a>
<FONT color="green">154</FONT>            Cluster&lt;T&gt; minCluster = null;<a name="line.154"></a>
<FONT color="green">155</FONT>            for (final Cluster&lt;T&gt; c : clusters) {<a name="line.155"></a>
<FONT color="green">156</FONT>                final double distance = point.distanceFrom(c.getCenter());<a name="line.156"></a>
<FONT color="green">157</FONT>                if (distance &lt; minDistance) {<a name="line.157"></a>
<FONT color="green">158</FONT>                    minDistance = distance;<a name="line.158"></a>
<FONT color="green">159</FONT>                    minCluster = c;<a name="line.159"></a>
<FONT color="green">160</FONT>                }<a name="line.160"></a>
<FONT color="green">161</FONT>            }<a name="line.161"></a>
<FONT color="green">162</FONT>            return minCluster;<a name="line.162"></a>
<FONT color="green">163</FONT>        }<a name="line.163"></a>
<FONT color="green">164</FONT>    <a name="line.164"></a>
<FONT color="green">165</FONT>    }<a name="line.165"></a>




























































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